Case Study of Bayesian Batch Optimization Strategies for Heterogeneous Catalysts in CO2 Hydrogenation

Abstract High-throughput and automated experimentation platforms combined with artificial intelligence are increasingly used to accelerate catalyst discovery. Batch Bayesian optimization (BO) (i.e., the simultaneous selection of multiple candidate experiments per iteration) is widely adopted for navigating large compositional spaces; however, its practical behavior under realistic high-throughput and batch constraints remains poorly understood. In this work, we performed a case study on four BO strategies to optimize the catalyst composition for the hydrogenation of CO2 to methanol. For each BO strategy, 48 catalysts were synthesized by co-precipitation, utilizing an automated synthesis platform, and evaluated in high-throughput fixed-bed reactors over seven batch iterations. The composition space comprised up to four metallic elements selected from a pool of nine. Although all BO strategies identified competitive catalyst formulations, most optimization trajectories rapidly converged toward distinct local high-performing regions that were strongly influenced by the initial random design and exhibited limited exploration of the broader compositional space. This case study provides practical insights into the limitations of BO for batch catalyst optimization and highlights their strong dependence on the initial random design, parameter space definition, and batch execution strategy.